{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "city='qingdao'\n",
    "city_cn='青岛'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developer Salary in Qingdao 青岛程序员工资调查\n",
    "我在4月1日到3日之间，抓取了某招聘网站的软件和互联网类招聘数据40万条，其中通过程序判断为程序员的14万条。地域方面，我选择了24个主要城市。不过本文只以一线城市为研究对象。这样是为了和我2017年6月的数据做对比。\n",
    "\n",
    "提到2017年的文章，现在居然还有很多人，把这篇文章拿出来炒作。对于社会来说，可气的是，他们直接把2017改成2019，就发表了，这不是骗人么？！对于我来说，可气的是，他们转载居然还冒充是原创，是可忍熟不可忍！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('../py')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data_original=pd.read_sql(sql=\"select * from _201903v2 where monthly_salary>0 and monthly_salary<80000 and YEAR(publish_date)=2019 and MONTH(publish_date)=3 and city='{}'\".format(city), con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_job_ids=['104660258','104142922','108434795','101357291','106253516','110368302','111391233','108665401','109277048'\n",
    "                  ,'73857191','108584955','102824950','102824949','111391233','110884556']\n",
    "data=data_original[~data_original.job_id.isin(error_job_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019年青岛程序员的平均工资为9343元，工资中位数为8500元，其中95%的人的工资位于4312到22250元之间。\n"
     ]
    }
   ],
   "source": [
    "salary_mean=data.monthly_salary.mean()\n",
    "salary_median=data.monthly_salary.median()\n",
    "q=np.quantile(data.monthly_salary,[0.025,0.975])\n",
    "print('2019年{}程序员的平均工资为{:.0f}元，工资中位数为{:.0f}元，其中95%的人的工资位于{:.0f}到{:.0f}元之间。'\n",
    "      .format(city_cn,salary_mean, salary_median, q[0], q[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2019, Developers in qingdao earn 9343 Yuan as average, the median is 8500 Yuan, 95% of them earn between 4312 and 22250 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print('In 2019, Developers in {} earn {:.0f} Yuan as average, '\n",
    "      'the median is {:.0f} Yuan, 95% of them earn between {:.0f} and {:.0f} Yuan.'\n",
    "      .format(city,salary_mean, salary_median, q[0], q[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15be7fcc710>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=179.31501020525735, pvalue=1.1540915805190702e-39)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(data.monthly_salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "null hypothesis: x comes from a normal distribution\n",
    "    \n",
    "p=0\n",
    "\n",
    "The null hypothesis can be rejected\n",
    "\n",
    "conclusion: data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cDLze7Dp/DbgiIlY3u5FXNG2D4oTU27z3RkRc3Bwb/ei0ZS1ZR//za/wunW0AOvVfHxGnRMQ64Hw6Jz1nveVJcwz8EeDa5vPT1+WS0Pxc7gT2Z+Znp71VYhuYq/5K28C8+n1mutcPOmf2v9M8ngJua9rPBh4GDjTPZzXtQecP4TwL7ANGpy3r9+icNDoIfKzftc1T85fo7O7+hM5vMzedyHqBUTr/gJ4F/prmIsSl8pij/n9s6nuCzj/6NdPmv62p5RmmfcuFzrdmvtu8d9uMbeqbzXr5MnBKv2ueUf+v0zk88QTwePO4uso2ME/9ZbaB+R5eMSxJhZU7HCRJepshIEmFGQKSVJghIEmFGQKSVJghIEmFGQKSVJghIEmF/R9CyFOUiFrl6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data.monthly_salary<40000].monthly_salary.hist()\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0,0), xytext=(0.5, 20), color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    count=[]\n",
    "    for feature in features:\n",
    "        salary_mean.append(data[data[feature]==1].monthly_salary.mean())\n",
    "        salary_median.append(data[data[feature]==1].monthly_salary.median())\n",
    "        count.append(data[data[feature]==1].shape[0])\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_category_column(data, prefix):\n",
    "    prefix_=prefix+\"_\"\n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    data.loc[:,prefix]=['']*data.shape[0]\n",
    "    for feature in features:\n",
    "        data.loc[data[feature]==1,prefix]=feature.replace(prefix_,'')\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_d8da0d58_69d0_11e9_a26f_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >career_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15bf1bee470>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career=get_sub_stats(data,'career_')\n",
    "data_career.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_d9e9942c_69d0_11e9_8797_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >8</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow0_col0\" class=\"data row0 col0\" >kotlin</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow0_col1\" class=\"data row0 col1\" >25000</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow0_col2\" class=\"data row0 col2\" >25000</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow0_col3\" class=\"data row0 col3\" >1</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.23%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >15</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow1_col0\" class=\"data row1 col0\" >ruby</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow1_col1\" class=\"data row1 col1\" >19166.7</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow1_col2\" class=\"data row1 col2\" >20000</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow1_col3\" class=\"data row1 col3\" >3</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow1_col4\" class=\"data row1 col4\" >0.69%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >9</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow2_col0\" class=\"data row2 col0\" >lua</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow2_col1\" class=\"data row2 col1\" >13833.3</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow2_col2\" class=\"data row2 col2\" >15000</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow2_col3\" class=\"data row2 col3\" >3</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow2_col4\" class=\"data row2 col4\" >0.69%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow3_col0\" class=\"data row3 col0\" >go</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow3_col1\" class=\"data row3 col1\" >13502.9</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow3_col2\" class=\"data row3 col2\" >11500</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow3_col3\" class=\"data row3 col3\" >29</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow3_col4\" class=\"data row3 col4\" >6.64%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >14</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow4_col0\" class=\"data row4 col0\" >python</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow4_col1\" class=\"data row4 col1\" >13205.9</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow4_col2\" class=\"data row4 col2\" >12500</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow4_col3\" class=\"data row4 col3\" >17</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow4_col4\" class=\"data row4 col4\" >3.89%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >10</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow5_col0\" class=\"data row5 col0\" >matlab</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow5_col1\" class=\"data row5 col1\" >12900</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow5_col2\" class=\"data row5 col2\" >9000</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow5_col3\" class=\"data row5 col3\" >5</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow5_col4\" class=\"data row5 col4\" >1.14%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >18</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow6_col0\" class=\"data row6 col0\" >swift</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow6_col1\" class=\"data row6 col1\" >12500</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow6_col2\" class=\"data row6 col2\" >11500</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow6_col3\" class=\"data row6 col3\" >7</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow6_col4\" class=\"data row6 col4\" >1.60%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >13</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow7_col0\" class=\"data row7 col0\" >php</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow7_col1\" class=\"data row7 col1\" >11830.4</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow7_col2\" class=\"data row7 col2\" >9500</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow7_col3\" class=\"data row7 col3\" >28</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow7_col4\" class=\"data row7 col4\" >6.41%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >5</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow8_col0\" class=\"data row8 col0\" >java</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow8_col1\" class=\"data row8 col1\" >10059.3</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow8_col2\" class=\"data row8 col2\" >9000</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow8_col3\" class=\"data row8 col3\" >135</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow8_col4\" class=\"data row8 col4\" >30.89%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >1</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow9_col0\" class=\"data row9 col0\" >cpp</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow9_col1\" class=\"data row9 col1\" >10017.5</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow9_col2\" class=\"data row9 col2\" >9000</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow9_col3\" class=\"data row9 col3\" >62</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow9_col4\" class=\"data row9 col4\" >14.19%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row10\" class=\"row_heading level0 row10\" >6</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow10_col0\" class=\"data row10 col0\" >javascript</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow10_col1\" class=\"data row10 col1\" >9818.18</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow10_col2\" class=\"data row10 col2\" >9000</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow10_col3\" class=\"data row10 col3\" >55</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow10_col4\" class=\"data row10 col4\" >12.59%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row11\" class=\"row_heading level0 row11\" >0</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow11_col0\" class=\"data row11 col0\" >c_sharp</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow11_col1\" class=\"data row11 col1\" >9155.43</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow11_col2\" class=\"data row11 col2\" >7500</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow11_col3\" class=\"data row11 col3\" >89</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow11_col4\" class=\"data row11 col4\" >20.37%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row12\" class=\"row_heading level0 row12\" >2</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow12_col0\" class=\"data row12 col0\" >delphi</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow12_col1\" class=\"data row12 col1\" >7666.67</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow12_col2\" class=\"data row12 col2\" >7000</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow12_col3\" class=\"data row12 col3\" >3</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow12_col4\" class=\"data row12 col4\" >0.69%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row13\" class=\"row_heading level0 row13\" >4</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow13_col0\" class=\"data row13 col0\" >haskell</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow13_col1\" class=\"data row13 col1\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow13_col2\" class=\"data row13 col2\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow13_col3\" class=\"data row13 col3\" >0</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow13_col4\" class=\"data row13 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row14\" class=\"row_heading level0 row14\" >7</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow14_col0\" class=\"data row14 col0\" >julia</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow14_col1\" class=\"data row14 col1\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow14_col2\" class=\"data row14 col2\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow14_col3\" class=\"data row14 col3\" >0</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow14_col4\" class=\"data row14 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row15\" class=\"row_heading level0 row15\" >11</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow15_col0\" class=\"data row15 col0\" >objective_c</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow15_col1\" class=\"data row15 col1\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow15_col2\" class=\"data row15 col2\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow15_col3\" class=\"data row15 col3\" >0</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow15_col4\" class=\"data row15 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row16\" class=\"row_heading level0 row16\" >12</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow16_col0\" class=\"data row16 col0\" >perl</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow16_col1\" class=\"data row16 col1\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow16_col2\" class=\"data row16 col2\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow16_col3\" class=\"data row16 col3\" >0</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow16_col4\" class=\"data row16 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row17\" class=\"row_heading level0 row17\" >16</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow17_col0\" class=\"data row17 col0\" >rust</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow17_col1\" class=\"data row17 col1\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow17_col2\" class=\"data row17 col2\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow17_col3\" class=\"data row17 col3\" >0</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow17_col4\" class=\"data row17 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row18\" class=\"row_heading level0 row18\" >17</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow18_col0\" class=\"data row18 col0\" >scrala</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow18_col1\" class=\"data row18 col1\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow18_col2\" class=\"data row18 col2\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow18_col3\" class=\"data row18 col3\" >0</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow18_col4\" class=\"data row18 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row19\" class=\"row_heading level0 row19\" >19</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow19_col0\" class=\"data row19 col0\" >typescript</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow19_col1\" class=\"data row19 col1\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow19_col2\" class=\"data row19 col2\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow19_col3\" class=\"data row19 col3\" >0</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow19_col4\" class=\"data row19 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row20\" class=\"row_heading level0 row20\" >20</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow20_col0\" class=\"data row20 col0\" >vba</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow20_col1\" class=\"data row20 col1\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow20_col2\" class=\"data row20 col2\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow20_col3\" class=\"data row20 col3\" >0</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow20_col4\" class=\"data row20 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_d9e9942c_69d0_11e9_8797_701ce71031eflevel0_row21\" class=\"row_heading level0 row21\" >21</th> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow21_col0\" class=\"data row21 col0\" >visual_basic</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow21_col1\" class=\"data row21 col1\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow21_col2\" class=\"data row21 col2\" >nan</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow21_col3\" class=\"data row21 col3\" >0</td> \n",
       "        <td id=\"T_d9e9942c_69d0_11e9_8797_701ce71031efrow21_col4\" class=\"data row21 col4\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15bf1bee320>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats(data,'pl_')\n",
    "data_pl.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_e522dc26_69d0_11e9_a744_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >edu_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15bf1bee588>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats(data,'edu_')\n",
    "data_edu.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'edu_phd'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-c00c16ad21d8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msalary_phd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0medu_phd\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmonthly_salary\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m10000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0msalary_master\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0medu_master\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmonthly_salary\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m10000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0msalary_bachelor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0medu_bachelor\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmonthly_salary\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m10000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0msalary_associate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0medu_associate\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmonthly_salary\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m10000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   4374\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4375\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4376\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4377\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4378\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'edu_phd'"
     ]
    }
   ],
   "source": [
    "salary_phd=data[data.edu_phd==1].monthly_salary * 10000\n",
    "salary_master=data[data.edu_master==1].monthly_salary * 10000\n",
    "salary_bachelor=data[data.edu_bachelor==1].monthly_salary * 10000\n",
    "salary_associate=data[data.edu_associate==1].monthly_salary * 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是7631，10898，15327，23264。中位数分别是7000， 10000，14583， 22500。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=13.844159585137783, pvalue=0.0007885605956567125)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=19.78684366975772, pvalue=1.0149296628970448e-05)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=130.1540133220867, pvalue=1.3145967703437656e-28)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=add_category_column(data,'edu')\n",
    "order=['phd','master','bachelor','associate']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(1.5, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >experience_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow0_col0\" class=\"data row0 col0\" >10</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow0_col1\" class=\"data row0 col1\" >24121.2</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow0_col2\" class=\"data row0 col2\" >22500</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow0_col3\" class=\"data row0 col3\" >11</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow0_col4\" class=\"data row0 col4\" >0.79%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >3</th> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow1_col1\" class=\"data row1 col1\" >15744.2</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow1_col2\" class=\"data row1 col2\" >15000</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow1_col3\" class=\"data row1 col3\" >114</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow1_col4\" class=\"data row1 col4\" >8.23%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow2_col1\" class=\"data row2 col1\" >11199.8</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow2_col2\" class=\"data row2 col2\" >10500</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow2_col3\" class=\"data row2 col3\" >269</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow2_col4\" class=\"data row2 col4\" >19.42%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >1</th> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow3_col0\" class=\"data row3 col0\" >1_3</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow3_col1\" class=\"data row3 col1\" >8894.07</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow3_col2\" class=\"data row3 col2\" >8500</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow3_col3\" class=\"data row3 col3\" >368</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow3_col4\" class=\"data row3 col4\" >26.57%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow4_col0\" class=\"data row4 col0\" >no</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow4_col1\" class=\"data row4 col1\" >7831.84</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow4_col2\" class=\"data row4 col2\" >7000</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow4_col3\" class=\"data row4 col3\" >623</td> \n",
       "        <td id=\"T_e86cb3d8_584a_11e9_84d1_701ce71031efrow4_col4\" class=\"data row4 col4\" >44.98%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2cd8eec6fd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats(data,'experience_')\n",
    "data_experience.style.format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience_10==1].monthly_salary * 10000\n",
    "salary_we_5_10=data[data.experience_5_10==1].monthly_salary * 10000\n",
    "salary_we_3_5=data[data.experience_3_5==1].monthly_salary * 10000\n",
    "salary_we_1_3=data[data.experience_1_3==1].monthly_salary * 10000\n",
    "salary_we_no=data[data.experience_no==1].monthly_salary * 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=add_category_column(data,'experience')\n",
    "order=['10','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >company_type_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >8</th> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow0_col0\" class=\"data row0 col0\" >state</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow0_col1\" class=\"data row0 col1\" >13464</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow0_col2\" class=\"data row0 col2\" >11000</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow0_col3\" class=\"data row0 col3\" >217</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow0_col4\" class=\"data row0 col4\" >16.82%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >9</th> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow1_col0\" class=\"data row1 col0\" >us_eu</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow1_col1\" class=\"data row1 col1\" >12774</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow1_col2\" class=\"data row1 col2\" >11500</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow1_col3\" class=\"data row1 col3\" >41</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow1_col4\" class=\"data row1 col4\" >3.18%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >3</th> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow2_col0\" class=\"data row2 col0\" >listed</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow2_col1\" class=\"data row2 col1\" >9699</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow2_col2\" class=\"data row2 col2\" >9000</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow2_col3\" class=\"data row2 col3\" >69</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow2_col4\" class=\"data row2 col4\" >5.35%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >0</th> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow3_col0\" class=\"data row3 col0\" >foreign</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow3_col1\" class=\"data row3 col1\" >9683</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow3_col2\" class=\"data row3 col2\" >8500</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow3_col3\" class=\"data row3 col3\" >35</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow3_col4\" class=\"data row3 col4\" >2.71%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >6</th> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow4_col0\" class=\"data row4 col0\" >public_institution</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow4_col1\" class=\"data row4 col1\" >8833</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow4_col2\" class=\"data row4 col2\" >7500</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow4_col3\" class=\"data row4 col3\" >3</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow4_col4\" class=\"data row4 col4\" >0.23%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow5_col0\" class=\"data row5 col0\" >private</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow5_col1\" class=\"data row5 col1\" >8333</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow5_col2\" class=\"data row5 col2\" >7000</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow5_col3\" class=\"data row5 col3\" >905</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow5_col4\" class=\"data row5 col4\" >70.16%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >7</th> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow6_col0\" class=\"data row6 col0\" >startup</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow6_col1\" class=\"data row6 col1\" >8102</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow6_col2\" class=\"data row6 col2\" >8000</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow6_col3\" class=\"data row6 col3\" >18</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow6_col4\" class=\"data row6 col4\" >1.40%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >4</th> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow7_col0\" class=\"data row7 col0\" >non_profit</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow7_col1\" class=\"data row7 col1\" >4500</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow7_col2\" class=\"data row7 col2\" >4500</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow7_col3\" class=\"data row7 col3\" >2</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow7_col4\" class=\"data row7 col4\" >0.16%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >1</th> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow8_col0\" class=\"data row8 col0\" >foreign_gov</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow8_col1\" class=\"data row8 col1\" >nan</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow8_col2\" class=\"data row8 col2\" >nan</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow8_col3\" class=\"data row8 col3\" >0</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow8_col4\" class=\"data row8 col4\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >2</th> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow9_col0\" class=\"data row9 col0\" >foreign_rep</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow9_col1\" class=\"data row9 col1\" >nan</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow9_col2\" class=\"data row9 col2\" >nan</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow9_col3\" class=\"data row9 col3\" >0</td> \n",
       "        <td id=\"T_eaf6bbac_584a_11e9_a98f_701ce71031efrow9_col4\" class=\"data row9 col4\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2cd8ef50550>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_type=get_sub_stats(data,'company_type_')\n",
    "data_company_type.style.format({\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >company_size_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >1</th> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow0_col0\" class=\"data row0 col0\" >1000_5000</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow0_col1\" class=\"data row0 col1\" >12679</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow0_col2\" class=\"data row0 col2\" >10500</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow0_col3\" class=\"data row0 col3\" >231</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow0_col4\" class=\"data row0 col4\" >16.68%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >5</th> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow1_col0\" class=\"data row1 col0\" >500_1000</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow1_col1\" class=\"data row1 col1\" >10684</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow1_col2\" class=\"data row1 col2\" >9000</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow1_col3\" class=\"data row1 col3\" >120</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow1_col4\" class=\"data row1 col4\" >8.66%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >4</th> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow2_col0\" class=\"data row2 col0\" >5000_10000</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow2_col1\" class=\"data row2 col1\" >10154</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow2_col2\" class=\"data row2 col2\" >10000</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow2_col3\" class=\"data row2 col3\" >13</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow2_col4\" class=\"data row2 col4\" >0.94%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >2</th> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow3_col0\" class=\"data row3 col0\" >150_500</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow3_col1\" class=\"data row3 col1\" >9313</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow3_col2\" class=\"data row3 col2\" >9000</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow3_col3\" class=\"data row3 col3\" >267</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow3_col4\" class=\"data row3 col4\" >19.28%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >6</th> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow4_col0\" class=\"data row4 col0\" >50_150</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow4_col1\" class=\"data row4 col1\" >8769</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow4_col2\" class=\"data row4 col2\" >7000</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow4_col3\" class=\"data row4 col3\" >453</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow4_col4\" class=\"data row4 col4\" >32.71%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >0</th> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow5_col0\" class=\"data row5 col0\" >10000</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow5_col1\" class=\"data row5 col1\" >8625</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow5_col2\" class=\"data row5 col2\" >9000</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow5_col3\" class=\"data row5 col3\" >18</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow5_col4\" class=\"data row5 col4\" >1.30%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >3</th> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow6_col0\" class=\"data row6 col0\" >50</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow6_col1\" class=\"data row6 col1\" >8014</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow6_col2\" class=\"data row6 col2\" >7000</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow6_col3\" class=\"data row6 col3\" >283</td> \n",
       "        <td id=\"T_ec6a16b4_584a_11e9_95c6_701ce71031efrow6_col4\" class=\"data row6 col4\" >20.43%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2cd8ee1a320>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_size=get_sub_stats(data,'company_size_')\n",
    "data_company_size.style.format({\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >industry_</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col3\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col4\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >7</th> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow0_col0\" class=\"data row0 col0\" >medical</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow0_col1\" class=\"data row0 col1\" >10187</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow0_col2\" class=\"data row0 col2\" >8500</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow0_col3\" class=\"data row0 col3\" >44</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow0_col4\" class=\"data row0 col4\" >2.49%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >10</th> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow1_col0\" class=\"data row1 col0\" >trade</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow1_col1\" class=\"data row1 col1\" >9950</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow1_col2\" class=\"data row1 col2\" >9000</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow1_col3\" class=\"data row1 col3\" >169</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow1_col4\" class=\"data row1 col4\" >9.55%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >6</th> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow2_col0\" class=\"data row2 col0\" >logistic</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow2_col1\" class=\"data row2 col1\" >9947</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow2_col2\" class=\"data row2 col2\" >9000</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow2_col3\" class=\"data row2 col3\" >41</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow2_col4\" class=\"data row2 col4\" >2.32%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >5</th> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow3_col0\" class=\"data row3 col0\" >gov</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow3_col1\" class=\"data row3 col1\" >9582</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow3_col2\" class=\"data row3 col2\" >9000</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow3_col3\" class=\"data row3 col3\" >60</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow3_col4\" class=\"data row3 col4\" >3.39%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >1</th> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow4_col0\" class=\"data row4 col0\" >computer</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow4_col1\" class=\"data row4 col1\" >9509</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow4_col2\" class=\"data row4 col2\" >8750</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow4_col3\" class=\"data row4 col3\" >1095</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow4_col4\" class=\"data row4 col4\" >61.90%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >3</th> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow5_col0\" class=\"data row5 col0\" >energy</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow5_col1\" class=\"data row5 col1\" >9210</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow5_col2\" class=\"data row5 col2\" >8000</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow5_col3\" class=\"data row5 col3\" >21</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow5_col4\" class=\"data row5 col4\" >1.19%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >0</th> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow6_col0\" class=\"data row6 col0\" >ads</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow6_col1\" class=\"data row6 col1\" >8921</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow6_col2\" class=\"data row6 col2\" >7000</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow6_col3\" class=\"data row6 col3\" >43</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow6_col4\" class=\"data row6 col4\" >2.43%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >8</th> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow7_col0\" class=\"data row7 col0\" >realestate</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow7_col1\" class=\"data row7 col1\" >8802</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow7_col2\" class=\"data row7 col2\" >8000</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow7_col3\" class=\"data row7 col3\" >29</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow7_col4\" class=\"data row7 col4\" >1.64%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >4</th> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow8_col0\" class=\"data row8 col0\" >finance</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow8_col1\" class=\"data row8 col1\" >8641</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow8_col2\" class=\"data row8 col2\" >8000</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow8_col3\" class=\"data row8 col3\" >26</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow8_col4\" class=\"data row8 col4\" >1.47%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >9</th> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow9_col0\" class=\"data row9 col0\" >service</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow9_col1\" class=\"data row9 col1\" >8579</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow9_col2\" class=\"data row9 col2\" >9000</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow9_col3\" class=\"data row9 col3\" >19</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow9_col4\" class=\"data row9 col4\" >1.07%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031eflevel0_row10\" class=\"row_heading level0 row10\" >2</th> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow10_col0\" class=\"data row10 col0\" >edu</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow10_col1\" class=\"data row10 col1\" >7855</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow10_col2\" class=\"data row10 col2\" >6000</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow10_col3\" class=\"data row10 col3\" >222</td> \n",
       "        <td id=\"T_ed9b58ba_584a_11e9_b1a4_701ce71031efrow10_col4\" class=\"data row10 col4\" >12.55%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2cd8e90bcc0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry=get_sub_stats(data,'industry_')\n",
    "data_industry.style.format({\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
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